A Discrete Event-Driven Model for Electric Vehicles Predictive Charging

A Discrete Event-Driven Model for Electric Vehicles Predictive Charging

Azizbek Ruzmetov (University of Technology of Belfort-Montbéliard, Belfort, France), Ahmed Nait-Sidi-Moh (Laboratory of Innovative Technologies, University of Picardie Jules Verne, Saint Quentin, France), Mohamed Bakhouya (Computer Science Department, International University of Rabat, Technopolis Sala el Jadida, Morocco), Jaafar Gaber (University of Technology of Belfort-Montbéliard, Belfort, France) and Marie-Ange Manier (University of Technology of Belfort-Montbéliard, Belfort, France)
DOI: 10.4018/IJARAS.2015070102
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Abstract

Very great research efforts have been made in the last decades to further develop and promote electric vehicles (EVs), their charging infrastructures, and operation techniques. However, little attention has been paid so far to the management of their charging planning, EVs assignment and mainly drivers' assistance to get into adequate charging stations (CSs). The charging planning and EVs assignment need to be predicted taking into consideration all operating constraints of charging systems including EV characteristics, status of CSs, road traffic, etc. This paper presents a discrete event driven model for EVs predictive charging. The authors mainly focus on behavior modeling of the charging system using (max, +) algebra and Petri nets. The model is then used to anticipate maximum charging times and charging rates of EVs while respecting their various constraints.
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EVs charging process and its dynamic are one of the main problems for promoting this type of vehicles. To tackle this problem, several approaches were proposed in the literature to reduce charging times while ensuring high performances (e.g. more autonomy and performances). For example, in (Vandael et al., 2011) a multi-agent system has been used to model and control the charging and discharging of plug-in hybrid electric vehicles (PHEVs). Furthermore, authors compared the reducing imbalance costs by reactive scheduling and proactive scheduling. Simulations showed that reactive scheduling is able to reduce imbalance costs by 14%, while proactive scheduling yields to a highest imbalance cost reduction of 44%.

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